System and method for generating real-time indicators in a trading list or portfolio

ABSTRACT

A trading platform computer system for detecting an abnormal trading condition of a security uses real-time and estimated values of one or more variables associated with the condition of the security to generate one or more analytic metrics that are compared to empirical distributions based on one or more peer groups for the security. An indicator can then be displayed to a trader as an indication of the abnormal condition.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of and claims priority to U.S. patentapplication Ser. No. 12/724,233, filed Mar. 15, 2010, which is aContinuation of and claims the benefit of priority to U.S. patentapplication Ser. No. 11/476,895, filed on Jun. 29, 2006, now U.S. Pat.No. 7,680,718, which issued Mar. 16, 2010, and which claimed priority toU.S. Provisional Application Ser. No. 60/694,668 filed Jun. 29, 2005,the entire contents of each of which are incorporated herein byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to investment portfolio managementsystems. In particular, the present application relates to acomputerized investment portfolio management system and method forrecognizing abnormal conditions in real-time, for a security beingtraded in a trading forum.

2. Description of the Related Art

Computerized trading systems exist that allow traders to monitor andexecute transactions in trading forums, such as the NASDAQ. Some tradingsystems include portfolio management functions and/or allow traders toexecute trade lists. For example, U.S. Published Patent Application No.2004/0181479, is directed to an investment portfolio optimizationsystem, method and computer program product, the entire contents ofwhich are incorporated herein by reference.

When trading a portfolio or a trade list, a small number of securitiescan sometimes skew the performance of the entire portfolio. It would beimportant to traders trading portfolios and trade lists to be able toidentify, preferably in real-time, such securities that could hurt theperformance of a portfolio or trade list, so that the trader can takethe appropriate actions to minimize potential losses to the portfolio ortrade list.

While computerized trading systems exist that allow traders to viewreal-time market data, such as price, volume and spread, and certainanalytics or quantitative metrics, such as moving averages, thesesystems fail to identify and effectively communicate to the traderabnormal conditions as they occur in real-time. Thus, there remains aneed for a system that can recognize abnormal conditions in real-timeand provide indicators that allow the trader to react quickly to theabnormal conditions.

SUMMARY OF THE INVENTION

It is therefore an object of the present invention to overcomedisadvantages of the prior art by providing systems and methods capableof identifying an abnormal condition of a security traded on anexchange.

A first aspect of the present invention is a system for generating anindicator of abnormality in the condition of a security traded on anexchange using real-time data from a remote source. The system includesa computer with a processor and a memory device storing a set of machinereadable instructions executable by the processor to receive inreal-time from the remote source a real-time value of a first variablerelated to a condition of the security, retrieve historical market datafor the security, and retrieve an empirical distribution of analytic orquantitative metrics for a peer group of the security, wherein theempirical distribution is based on a relationship of empirical values ofthe first variable for members of the peer group. The system thenestimates the value of the first variable based on the historical marketdata for the security, calculates an analytic metric (also referred toherein as an “analytic result”) based on a relationship between thevalue received in real-time and the value obtained by estimating, andcompares the analytic metric for the security with the empiricaldistribution of analytic metrics for the peer group to determine whetherthe condition of the security is abnormal.

In a preferred embodiment, the system receives in real-time from aremote source real-time values of a plurality of variables related to acondition of the security. Examples of the types of variables that maybe monitored in real-time by the system include, but are not limited to,trade price, trading volume, bid-ask spread, and depth. Both real-timevalues of the variables as well as estimates thereof based on historicaldata can be dynamically updated throughout the trading day. Examples ofthe types of analytics that can be calculated using the real-time valuesof the variables and estimates thereof include, but are not limited to,relative volatility, relative volume, and relative cost. The peer groupused to generate empirical distributions preferably includes a pluralityof securities having similar characteristics to the first security(e.g., volume, volatility, price, etc.). The system determines whetheror not an abnormal condition exists based on a comparison of theanalytic metrics to the empirical distribution of peer group analytics.For example, if the analytic metric is an unlikely deviation from theempirical distribution, an abnormal condition may be deemed to exist. Inone embodiment, the system can display an indicator to a trader alertingthe trader of the abnormal condition. The indicators can reflect thedegree and kind of condition. The system can include a single computerthat performs all of the above functions, or a network of computerswherein, e.g., certain functions are performed by a server and otherfunctions are performed by user workstations connected with the server.

Another aspect of the present invention is a method of detecting anabnormal condition of a security traded on an exchange. The methodincludes the steps of receiving in real-time a value of a first variablerelated to a condition of the security, estimating the value of thefirst variable based on historical market data for the security,calculating an analytic metric based on a relationship between the valueobtained by real-time monitoring and the value obtained by estimating,retrieving an empirical distribution of analytic metrics for a peergroup of the security, and comparing the analytic metric for thesecurity based on real-time data with the empirical distribution ofanalytic metrics for the peer group to determine whether the conditionof the security is abnormal.

In a preferred embodiment, the method includes the steps of receiving inreal-time from a remote source real-time values of a plurality ofvariables related to a condition of the security. Examples of the typesof variables that may be received in real-time according to the methodinclude, but are not limited to, trade price, trading volume, bid-askspread, and depth. Both real-time values of the variables as well asestimates thereof based on historical data can be received continuously(i.e., dynamically) throughout the trading day. Examples of the types ofanalytics that can be calculated using the real-time values of thevariables and estimates thereof include, but are not limited to,relative volatility, relative volume, and relative cost. The peer groupused to generate empirical distributions preferably includes a pluralityof securities having similar characteristics to the first security(e.g., volume, volatility, price, etc.). In determining whether or notan abnormal condition exists based on a comparison of the analyticrelation to the generated empirical distribution, the method may, forexample, deem an abnormal condition to exist if the analytic relation isan unlikely deviation from the empirical distribution. In oneembodiment, the method also includes the step of displaying an indicatorto a trader alerting the trader of the abnormal condition. Theindicators can reflect the degree and kind of condition.

Yet another aspect of the present invention is a computer programproduct for generating an indicator of abnormality in the condition of asecurity traded on an exchange. The computer program product includes adigital storage media and a set of machine readable instructions, storedon the digital storage media, which are executable by a computer toestablish communication between the computer and a remote source,receive a value of a first variable related to a condition of thesecurity in real-time from the remote source, retrieve historical marketdata for the security and estimate the value of the first variable basedon the historical market data for the security, calculate an analyticmetric based on a relationship between the real-time value of the firstvariable and the estimated value of the first variable, retrieve anempirical distribution of analytic metrics for a peer group of thesecurity, and compare the analytic metrics for the security with theempirical distribution of analytic metrics for the peer group todetermine whether the condition of the security is abnormal.

In a preferred embodiment, the computer program product is configuredsuch that, when it is executed, it causes a computer to receive inreal-time from a remote source real-time values of a plurality ofvariables related to a condition of the security. Examples of the typesof variables that may be received in real-time include, but are notlimited to, trade price, trading volume, bid-ask spread, and depth.Real-time values of the variables as well as estimates thereof based onhistorical data can be received continuously (i.e., dynamically)throughout the trading day. Examples of the types of analytics that canbe calculated using the real-time values of the variables and estimatesthereof include, but are not limited to, relative volatility, relativevolume, and relative cost. The peer group used to generate empiricaldistributions preferably includes a plurality of securities havingsimilar characteristics to the first security (e.g., volume, volatility,price, etc.). The computer program product can be configured todetermine whether or not an abnormal condition exists based on acomparison of the analytic relation to the generated empiricaldistribution. For example, if the analytic relation is an unlikelydeviation from the empirical distribution, an abnormal condition may bedeemed to exist. In one embodiment, the computer program product caninclude instructions executable by the computer to display an indicatorto a trader alerting the trader of the abnormal condition. Theindicators can reflect the degree and kind of condition. In anotherembodiment, the computer program product includes a second set ofinstructions executable by a second computer to receive indicators fromthe first computer and display them on the second computer.

Further objects and advantages of the present invention are discussedbelow with reference to the drawing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for identifying securitieshaving abnormal conditions according to an embodiment of the presentinvention.

FIG. 2 is a flow chart of a method for identifying securities havingabnormal conditions according to an embodiment of the present invention.

FIG. 3 is a screen shot of a trading desktop displaying indicators for alist of securities according to an embodiment of the present invention.

FIG. 4 is a screen shot of a trading desktop showing a window thatallows a user to set alert levels according to an embodiment of thepresent invention.

FIG. 5 is a screen shot of a trading desktop showing certain indicatorsshaded according to another embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

While the present invention may be embodied in many different forms, anumber of illustrative embodiments are described herein with theunderstanding that the present disclosure is to be considered asproviding examples of the principles of the invention and such examplesare not intended to limit the invention to the embodiments shown ordescribed herein.

Referring now to FIG. 1, a schematic diagram of a system 20 foridentifying abnormal conditions in a list of one or more securitiesaccording to an embodiment of the present invention is shown. The system20 includes a server 22 in communication with one or more userworkstations 24, for example via a direct data link connection or anetwork such as a local area network (LAN), an intranet, or theInternet. The server 22 and the work stations 24 can be computers of anytype so long as they are capable of performing their respectivefunctions as described herein. The computers can be the same, ordifferent from one another, but preferably each have at least oneprocessor and at least one memory device capable of storing a set ofmachine readable instructions (i.e., computer software) executable by atleast one processor to perform the desired functions, where by “memorydevice” is meant any type of media or device for storing information ina digital format on a permanent or temporary basis such as, for example,a magnetic hard disk, flash memory, an optical disk, random accessmemory (RAM), etc.

The computer software stored on the server (“server software”), whenexecuted by the server's processor, causes the server 22 to communicatewith the workstations 24 and one or more remote sources 26 of financialdata, such as data vendors, that offer real-time securities data in anelectronic format. The server software, when executed by the server'sprocessor, also causes the server 22 to perform certain calculations,described in greater detail below, using the real-time data from thedata vendors 26, as well as historical data about the securities andpeer group data, to determine whether an abnormal condition exists, andto send an indicator of the condition to one or more workstations 24.

The computer software stored on a workstation (“user software”), whenexecuted by the workstation processor, causes the workstation 24 toreceive indicators from the server 22 and to display the indicators to auser on a monitor, for example using a spreadsheet program, such asMicrosoft7 EXCEL7, an automated portfolio optimization program, such asACE7, or any other type of computer program capable of displaying a listof securities and data associated with the listed securities.

Historical securities data used by the system 20 to identify abnormalconditions can be received from a remote source 20, such as a datavendor, or from a local database 30 connected to, or maintained on, theserver 22. Empirical peer group data is preferably stored in a database32 maintained on, or otherwise accessible by the server 22.

The server 22 can be located at the user's facility or at a site remotefrom the user's facility. Communication between the server 22 and thedata vendors 26 and 28 can be accomplished via a direct data linkconnection or a network, such as a LAN, an intranet or the Internet. Inalternate embodiments, one or more workstations can be configured toperform the server functions such that a dedicated server is not needed.It will also be appreciated that workstations can be configured tocommunicate individually with data vendors and/or local databaseswithout being networked to a server or other workstations.

Operation of the system 20 is described with reference to the flow chartshown in FIG. 2, which illustrates a method 40 of identifying abnormalconditions in a list of one or more securities according to anembodiment of the present invention. The method begins at step 42, atwhich time the system initiates a number of steps involving receipt ofcertain information that will be used to determine abnormal conditionsof securities. At step 44, the system receives from a remote source,such as a data vendor, real-time values of one or more variablesassociated with a first condition of a security traded on an exchange,and stores at least some of the values in memory. Examples of the typesof variables that may be received in real-time from remote sourcesinclude, but are not limited to, trade price, trading volume, bid-askspread, and depth (i.e., ask size for buy orders or bid size for sellorders). In a preferred embodiment, values are received continuously inreal-time throughout a trading day and some of these values (e.g.,values received at predetermined intervals) are used to calculatecertain analytics as described in greater detail below. In a presentlypreferred embodiment, values received at a predetermined interval ofabout five seconds are used to compute the analytics. In addition, atleast some of the real-time values may optionally be stored in adatabase on the server or elsewhere for later reference as historicaldata.

At step 46, the system receives historical data on prior values of thefirst variable and stores the historical data in memory. For example,the historical data may include an average value of the first variablefor a particular time of day. In a preferred embodiment, the historicaldata includes a running average of values over a 21 day period. Thehistorical data can be received from one or more remote or local sourceswhenever it is needed, but is preferably received at predeterminedintervals throughout the day (e.g., about every five seconds). In apreferred embodiment, at least some of the historical data is maintainedin a database on the server and updated daily. At the beginning of thetrading day, the historical data in the database can be uploaded intomemory so that it can be accessed immediately by the system at any timeduring the trading day. Alternatively, all or some of the historicaldata may be received from a remote source, such as a data vendor.

At step 48, the system receives peer group data including values of thefirst variable for a group of securities that are considered to bewithin a peer group of which the first security is a member.Alternatively, the peer group data includes empirical distributions ofanalytic metrics based on historical values of the first variable forthe peer groups. Preferably, the peer group includes a plurality ofsecurities having similar characteristics to the first security. Forexample, characteristics such as industry or sector (e.g. basicmaterials, consumer cyclical, consumer non-cyclical, financials, health,industrials, information technology, resources, telecommunicationservices, and utilities), exchange (e.g., New York Stock Exchange,American Stock Exchange, and NASDAQ), and capitalization (e.g., large orsmall cap) can be used to define dozens of peer groups.

The peer group data can be received from one or more remote or localsources whenever it is needed. In a preferred embodiment, at least someof the peer group data is maintained in a database on the server andupdated at least quarterly. Like the historical data, at the beginningof the trading day, the peer group data in the database can be uploadedinto server memory so that it can be accessed immediately by the systemat any time during the trading day. Alternatively, all or some of thepeer group data may be received from a remote source, such as a datavendor. The peer group data may be accessed as frequently as desired butis preferably accessed at predetermined intervals throughout the day(e.g., about every five seconds).

While steps 44, 46 and 48 are shown in FIG. 2 as being performed inparallel, it will be appreciated that two or more of these steps can beperformed serially in any order. In a preferred embodiment, steps 44, 46and 48 are performed at predetermined intervals (e.g., about every fiveseconds) throughout a trading day.

At step 50, the system estimates a value for the first variable based onthe historical data received in step 46. For example, if a real-timevalue of the first variable is received at a particular time during atrading day and the historical data includes several values for thevariable at the same time of the day (e.g., within five minutes of thetime), the system might use an average value of the first variable forthe same time period as the estimated value for the first variable.

At step 52, the system calculates an analytic metric or result using thereal-time value of the first variable obtained in step 44 and theestimated value of the first variable generated in step 50. Someexamples of the types of analytic metrics (“analytics”) that can becalculated include, but are not limited to, relative volatility,relative volume, and relative price, each of which is described ingreater detail below. In general, the analytics involve some type ofratio or relationship between the real-time and estimated values of thevariable, providing an indication of how much the real-time valuedeviates from historical values of the variable.

At step 54, the system determines analytic metrics for each of thesecurities in the peer group using the data obtained in step 48. In oneembodiment, the peer group database includes historical values of thevariable for the peer group and the system uses the values to generatean empirical distribution of analytic metrics for the peer group.Alternatively, the database may include empirical distributions ofanalytic metrics organized by peer group and the system may receive anempirical distribution for an appropriate peer group in step 48 suchthat it is not necessary to generate an empirical distribution from rawdata each time the method is practiced.

At step 56, the system compares the analytic metric determined at step52 with the empirical distribution of analytic metrics determined at 54.For example, the system might determine where the analytic metric fallswithin the distribution in terms of percentile or standard deviation.

At step 58, the system uses the result of the comparison at step 56 togenerate an indicator of abnormality for the security. The indicator canbe qualitative (i.e., abnormal condition exists or does not exist) or itcan be more quantitative (e.g., degree of abnormality in a positive or anegative sense, etc.). For example, a qualitative determination may bemade that a quantity more than two standard deviations from the mean ofthe empirical distribution is abnormal. Alternatively, abnormality canbe measured on a scale (e.g., −5 to 5), with an indicator value of 0reflecting a neutral, average or normal condition.

The indicator of abnormality generated in step 58 is then sent to a userat step 60 for display by the user workstation. The process 40 is shownending at step 62, but it will be appreciated that the process can berepeated periodically throughout the trading day, e.g., every fewseconds or whenever the system receives a request for an update.

As described in greater detail below, the indicator is preferablydisplayed to the user in a spreadsheet format in which a list ofsecurities and associated information are arranged in rows and columnsAny icon or visual symbol can be employed as an indicator of abnormalconditions. For example, a plurality of up or down arrows could be usedto indicate whether the current trading condition is in an upper orlower percentile of the empirical distribution for the peer group, thenumber of arrows displayed reflecting the degree of abnormality. Boxes,bullets and/or bars can be used with or without a divider lineindicating the average or mean value of the empirical distribution.Charts, colors, shading and/or other visual aids can also be employed.

The present invention may be used in conjunction with or as an extensionto a trading platform. An exemplary trading platform with which thepresent invention can be used is described in co-owned U.S. patentapplication Ser. No. 10/166,719, filed on Jun. 12, 2002, the entirecontents of which are hereby incorporated by reference. That applicationdescribes an Agency Cost Estimator (“ACE”) method and system that allowa user to obtain price impact cost estimates for any pre-specifiedstrategy, and generates an optimal trading strategy subject to certainassumptions. ACE further allows a user to submit a proposed portfoliotrade execution and analyzes the execution according to a tradingstrategy algorithm. The present invention may act in cooperation with oras an extension to platforms like ACE by identifying, preferably inreal-time, securities that could hurt the performance of a portfolio ortrade list, so that the trader can take the appropriate actions tominimize potential losses to the portfolio or trade list.

An exemplary screen shot 70 of a screen from a trading platform ordesktop employing indicators of abnormality according to an embodimentof the present invention is shown in FIG. 3. In this example, streamingdata is used to populate a MICROSOFT® EXCEL® spreadsheet. As shown, thespreadsheet includes rows listing the securities in a trade list orportfolio and columns displaying ticker symbol 72, side 74 (i.e., buy orsell), size of the order 76, real-time and historical volume 78 and 80,real-time and historical 5-minute volatility 82 and 84, real-time andhistorical cumulative volatility 86 and 88, and performance 90 (i.e.,symbol v. sector 92 and symbol v. market 94). As shown in this example,indicators can be displayed on the spreadsheet to show abnormal tradingconditions of the listed securities. In particular, the spreadsheetincludes columns displaying indicators determined in accordance with thepresent invention, specifically volume indicators 96, 5-minutevolatility indicators 98, cumulative volatility indicators 100, symbolv. sector performance indicators 102, and symbol v. market performanceindicators 104. In this example, it was determined that security MRKcurrently has an abnormally high volume, while PZE currently isperforming poorly versus the sector.

In particular, the real-time volume for MRK at the time the screen shotwas taken was 59,007,900 as compared to the historical cumulative volume(e.g., over the last 21-days) which was only 15,943,258. The ratio ofcurrent to historical volume was therefore, 3.7. When this ratio iscompared to the empirical distribution for the peer group, it falls inthe 96% of the distribution spectrum; that is, it is only 4% likely thatthe real-time cumulative volume of 59 million would occur, andtherefore, the volume indicator is set to level 4 to indicate a severeabnormality. The trader can consider and react appropriately to thisimportant information. Thus, it is easy to understand how usefulindicators generated according to the present invention can be to atrader.

As mentioned previously, any icon or visual indicator can be employed tosignify abnormal conditions. For example, a plurality of up or downarrows could be used to indicate whether the current condition is in anupper or lower percentile of the distribution of metrics. Boxes, bars orbullet points could be used with or without a divider line indicatingthe average or mean value of the empirical distribution. Charts, colors,shading or other visual aids can also be employed.

Indicators generated in accordance with the present invention could befurther used to alert or warn the user/trader of dangerous conditions.For example—changes in volume patterns may require changingparticipation rates in a portfolio, or when a certain symbol isoutperforming a corresponding sector index it might be necessary toexecute a “short” based on the belief that it will return to historicallevels.

Referring to FIG. 4, the system can be configured to allow the user toassign alert level set points used to trigger such an alarm or toconfigure the indicators display characteristics (e.g., at what rangethe indicators change display levels—such as from 3 to 4 “dots” or“bars”). As shown, a pop-up box or window 106 is displayed over thespreadsheet 70 and includes tabs for volume 108, volatility 110,performance 112, etc. Ranges can be set based upon the empiricaldistribution spectrum (in percent), but in the case of normaldistributions, could be set by standard deviation. Note that the entireline for symbol CPN is highlighted and corresponds to the pop-up box106. Levels can be set for each individual indicator or globally for allindicators.

It should be understood that market data can be used to generateindicators according to the present invention. Preferably, LEVEL 1and/or LEVEL 2 data is collected in real-time for input into a systemperforming the analytics to generate the indicators.

Real-time and historical market data can be collected for each securityin a portfolio or trade list, or for all securities. Accordingly, asystem implementing indicators according to the present invention shouldbe configured to receive such information and preferably will include a“live feed” or “stream” of data. Empirical distributions may begenerated by any means and should be accessible to the system.Alternatively, a means for generating empirical distributions locallycould be included, which could generate such distributions dynamicallybased on up-to-date historical data.

The peer groups can be defined statically (i.e., predetermined) on thebasis of characteristics such as, e.g., sector, exchange, andcapitalization, or dynamically, by identifying securities having similarcharacteristics to a selected security such as, e.g., volume,volatility, price, etc.

FIG. 5 is a screen shot of another embodiment of the present inventionwherein data is shaded or color coded on the trading desktop to indicateabnormal trading conditions for a security. For example, in row 17 ofthe spreadsheet, the symbol ATI is shown to have abnormally highvolatility by color coding cells 116 a and 116 b; and in row 21, thesymbol LU is shown to have abnormally high volume by color coding cells118 a and 118 b. The decision whether or not to shade or color code acell to indicate an abnormal condition is preferably based on acomparison of the type described above in connection with method step 56in FIG. 2.

No particular system configuration must be used to implement the presentinvention. It is recognized that it may be easiest to use a web based orclient-server architecture wherein analytics are programmed onto acentral server or onto a plurality of servers. Analytics can also becalculated on the client side as well.

Real-time and historical data is typically obtained from data vendors,via, for example, a dedicated line or over an electronic data network.Level 1 and/or level 2 data can be used. Examples of level 1 datainclude, but are not limited to, FITCH, ISSM, TAQ, TORQ or so-called“ticker” information. Level 2 data offers the unique ability to observethe amount of market liquidity, the price structure, and the quantity ofunexecuted displayed limit orders at any given time of the day for anymarket venue. It contains trade as well as all order information (ordermessages about additions, modifications and cancellations) and thusparticularly, all quote information beyond the best levels. Real-timeanalytics are preferably input into a real-time data and analytics API.Qualitative signals are derived from the historical and real-time dataand output to the client, which in this case is an EXCEL or ordermanagement system (“OMS”) client. The client interface is preferablyrobust and flexible enough to enable the users to create their owncustom analytics.

Analytics

One skilled in the art will understand that the analytics used togenerate indicators according to the present invention can vary. Asdescribed above, historical and real-time data may be collected from anumber of sources for the calculation of analytics generating stocksignals such as Relative Volume, Relative Volatility, etc. Indicatorscould be generated according to the present invention for any stocksignal that is displayed on a trading desktop.

Empirical distributions can be generated a number of ways for a numberof periods. Quarterly distributions are contemplated for simplicity. Ofcourse, longer or shorter periods could be used for generatingdistributions. Historical data can be used to generate empiricaldistributions for predetermined (i.e., static) or dynamically determinedpeer groups. Preferably, peer groups are created for use with allanalytics, however, one will understand that different peer groups couldbe used for different analytics.

A number of exemplary analytics are described below which can beimplemented according to the present invention. The below discussion ofanalytics is by no means intended to limit the present invention to onlythose analytics described and one having ordinary skill in the art willreadily understand that other analytics could be used to generateabnormal condition indicators according to the present invention.

Relative Volatility

${{{Relative}\mspace{14mu} {Risk}} = \frac{r_{\tau}}{\sigma_{\tau}}},$

where r_(τ) is(a) today's return over time τ−Δτ through τ, and(b) today's return from “open” to τ; andσ_(τ) is the historical volatility of the same time period in a day.

For meaningful analytics, the time interval Δτ in (a) above should beappropriately set; for example, 5-minute intervals could be chosen. Thehistorical average statistics can be computed based on the analyticswhich provide 30-minute bin volatility distributions. The historicalaverage statistics are preferable dynamic or “moving” averages.Historical estimates may be updated periodically or on a monthly basis.

Relative volatility or risk can be based on the following data for eachstock:

60 most recent prior day's closing prices,

daily (forward-looking) relative volatility, and

intra-day mid-quote volatility

The intra-day mid-quote volatility is given by:

$\sqrt{\frac{1}{n - 1}{\sum\limits_{i = 2}^{n}\frac{( {\ln ( {P_{i}/P_{i - 1}} )} )^{2}}{t_{i} - t_{i - 1}}}},$

where

-   -   n is the total number of valid quotes for that day;    -   P_(i) and t_(i) are, respectively, a midpoint and time (in        seconds) of the i'th valid quote. If the first quote was before        regular opening time, we set t₁ to the regular opening time.

Valid quotes are defined as quotes with no or valid condition codes that

-   -   (a) have strictly positive bid and ask prices.    -   (b) have strictly larger ask than bid prices (crossed or locked        quotes should be excluded).    -   (c) have strictly positive bid and ask sizes for specified        markets.    -   (d) are not second level quotes (a second level quote is defined        as a quote that has a market maker i.d.).    -   (e) have time stamps within regular trading hours or, for USA        only, represents the last quote prior to regular opening time        reported from the primary market for the stock.

Stock-specific and aggregated binned volatility “distributions”(ζ_(j)=0, 1, . . . , 13, can be retrieved or derived. The value ζ_(o)represents the weight for the overnight volatility. Note that the sum ofthe weights of (ζ_(j)) does not equal to one, however the sum of theweights for (ζ_(j) ²) does. Certain stocks do not trade at a volumesufficient such that meaningful statistics can be generated from thedata relating solely to that stock. Therefore, stocks that do not havespecific volatility numbers are aggregated by “liquidity bands”.Volatility is, then, calculated for this group. For example, all stocksthat trade between 5M-10M shares a day become one group, etc.

In addition, the historical daily volatility of a stock should becomputed. The daily volatility σ with capped maximum return value can becomputed based on the split-adjusted daily returns of the last 60trading days and is defined as:

$\sigma = ( {\frac{1}{( {T} ) - 1}{\sum\limits_{t \in T}^{\;}( {r_{t} - \overset{\_}{r}} )^{2}}} )^{1/2}$

where r_(t)=(q_(t)−q_(t-1))/q_(t-1′) r=1/|T|·Σ_(τεT)r_(1′) T={t=1, . . ., 60|r₁ exists} and |T| is the cardinal number of set T.

The term “with capped maximum return value” means that the largestreturn in absolute value is capped to the second largest return inabsolute value, then adjust by sign. That is, if the largest return inabsolute value is −0.3 and the second largest in absolute value is 0.2,then the largest value will be replaced with −0.2. In the case that lessthan 10 return observations (i.e. if |T|<10) exist, the forward-lookingvolatility can be used.

The real-time data for the relative volatility measure (a), includes thelast trade price P_(τ) within the last 5 minutes and the last tradeprice P_(τ-Δτ) before P_(τ). In the case that there are no trades in thelast 5 minutes or if P_(τ-Δτ) does not exist or was reported more than30 minutes prior to P_(τ), the return is set equal to 0; for therelative volatility measure (b), data includes the last trade priceP_(τ) and today's open price P_(open). In the case that there are notrades, the return is set equal to 0.

Intraday return can be represented by r_(t)=(P_(τ)−P_(τ-Δτ))/P_(τ-Δτ).Return from open to τ can be represented byr_(t)=(P_(τ)−P_(open))/P_(open).

For Historical volatility within one regular bin i (for relativevolatility measure (a)), the bin volatility is defined as σ=ζ_(i)σ. The5-minute volatility is set to be equal to σ_(τ)=(Δτ/30)^(1/2)σ_(i).

For Historical volatility across two regular bins i and j (for relativevolatility measure (a)), if the interval τ−Δτ through τ crosses tworegular bins as Δτ=Δ_(i)+Δ_(j) then the 5-minute volatility becomes

$\sigma_{\tau} = {( {{\frac{\Delta \; i}{30}\sigma_{i}^{2}} + {\frac{\Delta \; i}{30}\sigma_{j}^{2}}} )^{1/2}.}$

For Historical volatility at the open (for relative volatility measure(a)), if τ is between 9:30 am and 9:35 am, r_(τ) is defined as thereturn over time from open to τ and

${\sigma_{\tau} = ( {\frac{\Delta}{30}\sigma_{1}^{2}} )^{1/2}},$

where Δ is the number of minutes from 9:30 to τ.

For Historical volatility from open to τ (for relative volatilitymeasure (b)), if τε bin I and Δτ is the time from the beginning of thebin i to τ, the historical volatility from open to τ is defined as

$\sigma_{\tau} = {( {{\sum\limits_{j = 1}^{i - 1}\sigma_{j}^{2}} + {\frac{\Delta \; \tau}{30}\sigma_{i}^{2}}} )^{1/2}.}$

For the mapping of quantitative statistics into qualitative signals,“cutoffs” can be group-specific, where the groups are classified bysector (ITG Industry Classification based on FTSE), market, and addv.Cut-offs are parameters used to define peer groups—stocks that aregrouped together for specific indicators. For example, all stocks thatare in the information technology sector and traded on NYSE and have anAverage Daily Dollar Volume (ADDV) bigger than X can be groupedtogether.

Relative Volume

${{{Relative}\mspace{14mu} {volume}} = \frac{{Vol}_{\tau}}{V_{\tau}}},$

where Vol_(τ) is today's actual trading volume from open to τ, today'sactual trading volume in the period (τ−5 min, τ) and Vτ is thehistorical average volume of the same time period within a day.

Relative volume is analogous to relative volatility. Relative volume canbe calculated with the following data for each stock: prior day'sclosing price q, 21-days median dollar volume (addv) DV. Analogously toACE, the 21-day median share volume can be defined as V=DV/q.

Stock-specified and aggregated 30-minute (bin) volume distribution(Ψ_(j)) can be retrieved or derived.

The real-time data includes the trade sizes of all semi-valid tradesfrom open to τ (from τ−5 min to τ) or, alternatively, the cumulative(semi-valid) trading volume from open to τ (from open to τ) on a minute(or a few seconds) basis.

Assume that τ is in bin I and Δτ minutes after the end of bin i−1. Wedefine the historical cumulative trading volume from open to τ as

${V_{\tau} = {( {{\sum\limits_{k = 1}^{i - 1}\psi_{k}} + {\frac{\Delta \; \tau}{30}\psi_{i}}} ) \cdot V}},$

from τ−5 min to τ as

$V_{\tau}\{ {\begin{matrix}{{{{\frac{5}{30}*\psi_{1}*V},{\Delta \; \tau}}\rangle}5\mspace{14mu} \min} \\{{( {{\Delta \; \tau_{i}\psi_{i - 1}} + {\Delta \; \tau_{1}\psi_{i}}} )\frac{V}{30}},{otherwise}}\end{matrix},} $

where Δτ₁ and Δτ₂ is the tem spent in bin i−1 and i, respectively. Inparticular, Δτ₁+Δ₂=5 min.

Note that empirical distributions of relatively illiquid stocks may havea substantial weight close to zero (especially for 5-minute relativevolume distribution).

Relative Cost

${{{Relative}\mspace{14mu} {cost}} = \frac{{PI}_{\tau}}{{ACE}\; \tau}},$

where PI_(τ) is the price impact estimate at time τ for a particularorder size and ACE_(τ) is the ACE estimate for executing the same ordersize in the bin corresponding to time_(τ).

Price Impact Model

Price impact estimates for a particular time and order size can bederived from the formula: Price Impact=Spread Component+Liquid DemandComponent, where

$\mspace{79mu} {{{{Spread}\mspace{14mu} {Component}} = {{Max}( {\frac{{Spread}^{c_{1}}}{2},{0.35*{Spread}}} )}},{and}}\;$${{Liquidity}\mspace{14mu} {Demand}\mspace{14mu} {Component}} = {C_{2}*( \frac{{{Order}\mspace{14mu} {Size}} - {Depth}}{{Volume}\mspace{14mu} 30\mspace{14mu} \min} )C_{3}*{Volatility}\mspace{14mu} 30\mspace{14mu} \min \mspace{14mu} {C_{4}.}}$

If the Order Size is less than the Depth, there is no PI forecasting.

Real-time information required for forecast includes:

-   -   Spread—Most recent Bid-Ask spread in cents,    -   Order size—Order size in round lots with tolerable Fractional        number (Trim the original order size to 50,000 shares if it's        more than 50,000 for robustness.)    -   Depth—Ask Size—For Buy Orders (Round lot), Bid Size—For Sell        Orders (Round lot)—, Volume30 min—    -   MAX (Total share volume in the past 30 minutes, LowerThreshold);        -   If after 10:00 am: LowerThreshold=ADV*(Pct1*T1+Pct2*T2)/3);        -   If before 10:00 am: LowerThreshold=ADV*(Pct2*T2)/3);            -   where

T2=1/30*Minutes in the current 30-min bin and

T1=1−T2

-   -   -   -   Pct1 and Pct2, average volume profiles of previous and                current 30-minute bins.

    -   Volatility30 min—Difference in dollar between highest and lowest        of ASK prices for BUY orders and BID price for SELL orders in        the past 30 minutes, with bounds (0.02, BID/20) (Note that 0.02        is used for market with available quote).

In addition to the real-time feed, the model requires the parameterestimates of C₁, . . . , C₄ are calculated based on the combination ofmarket (“UN” and “UQ”) and two market cap levels (Lower and UpperCapital threshold). Altogether there are four models, all with the samefunction specification, that are fitted for each of the foursub-samples. For the model, stocks that traded primarily on the “US”market uses the same parameter which are associated with the “UN” marketand stocks that traded primarily on the “UR” market uses the sameparameters as that of stocks primarily traded on the “UQ” market.

Volume30 min can be obtained as snapshot of intra-day marketconsideration and is inaccessible for the early trading before 10:00 am.To solve this problem, the scaling rules set up as below are adopted:

For orders before 9:45 pm: Volume30 min=Volume fromopen+(T1/30)*Empirical Volume30 min before 10:00 am

For orders after 9:45 pm: Volume30 min=Volume from open*(30/T2) before10:00 pm, where, T2 is the time window from open to order submission andT1 is the window left from T2. Volume30 min of early trading is alsosubject to Lower Threshold=ADV*(Pct*T2/30)/3).

Since the models are fitted separately, a significant degree ofdiscontinuity is present when a stock crosses the market cap boundary.To solve this issue, sudden changes can be smoothed out at thisboundary. Specifically, the boundary value refers respectively to(lowercapthreshold) for large cap stocks and (upperercapthreshold) forsmall cap ones in the deliverable.

The Large Cap Model is to get prediction (PI_(τ) ^(L arg eCapModel)) ifmarket cap is 20% more of the boundary value. The Small Cap Model isused to get prediction (PI_(τ) ^(SmaallCapModel)) if market cap is 20%less of the boundary value. Otherwise, the predictions from large andsmall cap models can be mixed as PIτ=

$\frac{\begin{matrix}{{( {{1.2*{Boundry}} - {MKTCAP}} )*( {PI}_{\tau}^{SmallCapModel} )} +} \\{( {{MKTCAP} - {0.8*{Boundary}}} )*( {PI}_{\tau}^{LargeCapModel} )}\end{matrix}}{0.4*{Boundary}}$

Market cap levels mentioned above are defined through lowercapthresholdand upperercapthreshold.

ACE

Numerous stock data is required to ACE calculations (see, the '719application referenced above), including:

primary exchange,

price impact estimates based on 1 year of tick data,

price improvement estimates based on execution data,

intra-day volume,

volatility and spread distributions,

daily (forward-looking) relative volatility, and

21-days median dollar volume (addv), and average relative spread.

ACE cost estimates are derived using a one-bin strategy, i.e. the wholeorder size for a stock is executed in the bin the time τ belongs to. AllACE calculations use the latest available ACE files and executables andare applied to the listed case (i.e., ACE costs should be computed oncefor the whole list of stocks rather than for each stock individually).

Cutoffs are group-specific, where the groups are classified by market,(forward-looking) volatility and addv.

Tracking Statistics

Tracking Statistics is defined as

${\frac{( {1 + r_{\tau}} )}{( {1 + {Index}_{\tau}} )} - 1},$

where r_(τ) is the return of stock I over time intervals τ−Δτ and τ, andIndex_(τ) is the return of benchmark such as market, sector, industry,and ETF (Exchange Traded Fund) over the same interval. Two types oftracking statistics are contemplated: a first is for the five-minuteinterval (Δτ=300 seconds) and a second is for cumulative excess returnsince yesterday's close (Δτ is not fixed, but depends on the time of thelast trade during the previous trading day). Since the real-time returnsfor market, sector, and industry are not always available, relevant ETFscan be identified in order to approximate returns of interest. Anexemplary list of ETF's that can be used is presented in the tablebelow:

TABLE I preferred pick alternative pick Sector ticker vendor/advisorticker vendor/advisor Materials IYM DJ VAW Vanguard Consumer IYC DJ VCRVanguard discretionary Consumer XLP SPDR IYK DJ staples Financials XLFSPDR IYF DJ Healthcare IYH DJ — — Industrials XLI SPDR IYJ DJInformation XLK SPDR IYW DJ technology Resources IGE GSachs — — TelecomIYZ DJ — — Utilities XLU SPDR IDU DJ

The excess return could be brought together with an indicator whetherthe stock moves the same direction of the index. Mapping fromquantitative statistics into qualitative signals may be based onseparate distributions when the stock moves the same direction as theindex and different direction. Further, the cutoffs are group-specificwhere the groups are classified by market cap and sector.

Market capitalization groups are “small” (bottom 30% of NYSE marketcapitalization), “medium” (middle 40% of NYSE market capitalization) and“big” (top 30% of NYSE market capitalization). NYSE marketcapitalization is recorded as of the first trading day of the currentquarter. Current cutoff values (30% and 70-% NYSE size percentiles inmillions) will be presented as the “lower cap threshold” and “upper capthreshold” of “medium” size stocks.

From the above, it will be appreciated that the present inventionprovides a system and a method for generating one or more quantitativeand qualitative indicators that aim to point out abnormalities in atrade list. In one sense, the present invention is a “tail management”tool that attempts to predict or warn the traders on abnormalchanges/movements that happen in real-time to certain stocks in thetrade list. Analytics are based on relations between historical andreal-time data. In particular, the analytics are calculated as a ratioor a relative measurement of a real-time variable (such as volumetraded, volatility, relative return tracking, etc.) related to a firstsecurity traded on an exchange to an estimate of the real-time variablebased on historical data. Both the real-time value of the variable aswell as the estimate of it based on historical data can be dynamicallyupdated throughout the day. An empirical distribution for the analyticcan be generated based on a peer group for the first security. The peergroup preferably includes a plurality of securities having similarcharacteristics to the first security (e.g., sector, exchange, marketcapitalization, average daily trading volume, volatility, price, etc.).The method includes a step of comparing the calculated ratio or relationto the generated empirical distribution, and then determining whether ornot an abnormal condition exists based on the comparison. An indicatorcan be displayed to a trader as an indication of the abnormal condition.

Thus, a number of preferred embodiments have been fully described abovewith reference to the drawing figures. Although the invention has beendescribed based upon these preferred embodiments, it would be apparentto those of skill in the art that certain modifications, variations, andalternative constructions could be made to the described embodimentswithin the spirit and scope of the invention. For example, as explainedabove, numerous other analytics could be calculated for the purpose ofgenerating indicators of abnormal trading conditions for a securityaccording to the present invention.

1. A trading platform comprising: a trading interface executed on atrader desktop device; and a server computer coupled to one or moreelectronic communication networks, wherein said server computer isconfigured to: receive current market data from a remote data source viaa digital communications link including a current value of a firstvariable related to a condition of a first security, estimate the valueof the first variable based on historical market data for the firstsecurity, calculate a first analytic metric based on a relationshipbetween the current value of the first variable and the estimated valueof the first variable, receive a plurality of values of the firstvariable related to the condition of each of a plurality of securitiesin a peer group of the first security, estimate a plurality of values ofthe first variable based on historical market data for each of theplurality of securities in the peer group, calculate an empiricaldistribution of analytic metrics for the peer group, wherein theanalytic metrics are based on relationships between the values of thefirst variable retrieved for members of the peer group and the valuesobtained by estimating, compare the first analytic metric for the firstsecurity with the empirical distribution of analytic metrics for thepeer group to determine whether the condition of the first security isabnormal, and provide to said trading interface via said electroniccommunication network an indicator for display, wherein said indicatoris reflective of abnormality in the condition of the first security. 2.The trading platform of claim 1, wherein said trading interface isconfigured to: receive via an electronic communication network saidindicator from said server, and display said indicator to a user of saidtrading interface.
 3. The trading platform of claim 1, wherein saidserver is further configured to periodically select a real-time value ofa first variable from a stream of real-time values throughout a tradingday; wherein the historical market data is stored in a database, andsaid server computer is further configured to periodically update thedatabase using the stream of real-time values.
 4. The trading platformof claim 1, wherein the server is further configured to calculate amoving average of the value over a predetermined period of time usinghistorical market data.
 5. The trading platform of claim 1, wherein theserver is configured to calculate an analytic metric by calculating atleast one of relative volatility, relative volume, and relative cost. 6.The trading platform of claim 1, further comprising a peer groupdatabase storing empirical distributions of analytic metrics; whereinthe server is further configured to retrieve the empirical distributioncorresponding to a peer group of the first security from the peer groupdatabase; wherein the peer group database stores values of the firstvariable for a plurality peer groups; and wherein the server is furtherconfigured to generate the empirical distribution of analytic metricsusing values from the peer group database.
 7. The trading platform ofclaim 1, wherein said server computer is further configured to retrievea plurality of values of the first variable periodically throughout atrading day at predetermined intervals, and each time select a real-timevalue of the first variable.
 8. The trading platform of claim 6, whereinsaid plurality of empirical distributions are based on a plurality ofdifferent peer groups.
 9. The trading platform of claim 1, wherein theserver is further configured to define a peer group and gather datarelated to the peer group to generate the empirical distribution;wherein the peer group is either defined statically based on at leastone of industry sector, exchange, and market capitalization, ordynamically based on real-time data related to the peer group; andwherein the peer group is defined dynamically based on real-time datarelated to the peer group.
 10. The trading platform of claim 1, whereinthe server is further configured to determine where the calculatedanalytic metric falls within the empirical distribution; and wherein theserver is further configured to describe where the calculated analyticmetric falls within the empirical distribution in terms of percentile orin terms of standard deviation.
 11. The trading platform of claim 10,wherein the server is further configured to describe where thecalculated analytic metric falls within the empirical distribution interms of standard deviation.
 12. The trading platform of claim 1,wherein said indicator is reflective of the degree of abnormality of thesecurity condition based on said comparison of the first analytic metricfor the first security with the empirical distribution of analyticmetrics for the peer group, and includes at least one of a numericalvalue related to the degree of abnormality, a number of symbols, whereinthe number of symbols is related to the degree of abnormality, and acolor coded numerical value related to the degree of abnormality;wherein symbols include at least one of up and down arrows and bars, theup arrows representing a condition in the upper half of the empiricaldistribution and the down arrows representing a condition in the lowerhalf of the empirical distribution, the bars being on either side of adivider to represent a condition in the upper or lower half of thedistribution.
 13. The trading platform of claim 1, wherein said servercomputer is further configured to execute a trade based on saidcomparison of the first analytic metric for the first security with theempirical distribution of analytic metrics for the peer group.
 14. Thetrading platform of claim 2, wherein said trading interface is furtherconfigured to execute a trade based on said comparison of the firstanalytic metric for the first security with the empirical distributionof analytic metrics for the peer group.